Explainability-Inspired Layer-Wise Pruning of Deep Neural Networks for Efficient Object Detection

TL;DR

This paper introduces an explainability-inspired, layer-wise pruning method for deep neural networks in object detection, improving efficiency while maintaining accuracy by using attribution techniques to identify less important layers.

Contribution

The work presents a novel attribution-based pruning framework that outperforms traditional magnitude-based methods in selecting layers for pruning in object detection networks.

Findings

Improved accuracy-efficiency trade-offs across multiple architectures.

10% speed increase for ShuffleNetV2 with negligible accuracy loss.

Preservation of baseline mAP for RetinaNet with minimal impact on inference speed.

Abstract

Deep neural networks (DNNs) have achieved remarkable success in object detection tasks, but their increasing complexity poses significant challenges for deployment on resource-constrained platforms. While model compression techniques such as pruning have emerged as essential tools, traditional magnitude-based pruning methods do not necessarily align with the true functional contribution of network components to task-specific performance. In this work, we present an explainability-inspired, layer-wise pruning framework tailored for efficient object detection. Our approach leverages a SHAP-inspired gradient--activation attribution to estimate layer importance, providing a data-driven proxy for functional contribution rather than relying solely on static weight magnitudes. We conduct comprehensive experiments across diverse object detection architectures, including ResNet-50, MobileNetV2, ShuffleNetV2, Faster R-CNN, RetinaNet, and YOLOv8, evaluating performance on the Microsoft COCO 2017 validation set. The results show that the proposed attribution-inspired pruning consistently identifies different layers as least important compared to L1-norm-based methods, leading to improved accuracy--efficiency trade-offs. Notably, for ShuffleNetV2, our method yields a 10\% empirical increase in inference speed, whereas L1-pruning degrades performance by 13.7\%. For RetinaNet, the proposed approach preserves the baseline mAP (0.151) with negligible impact on inference speed, while L1-pruning incurs a 1.3\% mAP drop for a 6.2\% speed increase. These findings highlight the importance of data-driven layer importance assessment and demonstrate that explainability-inspired compression offers a principled direction for deploying deep neural networks on edge and resource-constrained platforms while preserving both performance and interpretability.